Experiment with pipeline samples→ https://goo.gle/2QuyMSO
Want to learn how to create an ML application from Kubeflow Pipelines? In this episode of Kubeflow 101, we show you how to build a Kubeflow Pipeline from the ML model we explored in the last episode. Moreover, we give you a walkthrough of how to create, test and deploy your ML application in Kubeflow Pipelines. Watch to learn how Kubeflow Pipelines can bring orchestration to complex workflows when working with ML applications.
Last episode → https://goo.gle/2FKcLNF
Timestamps:
0:00 – Intro
0:40 – Pipeline steps overview
1:09 – Import dependencies, define constants
1:45 – Download trainer data
2:10 – Train the model
3:04 – Deploy the model
3:22 – Define and submit the Kubeflow pipeline
3:56 – Compile and share the pipeline
4:26 – Conclusion
Watch more episodes of Kubeflow 101 → https://goo.gle/3cqY2lR
Subscribe to the GCP Channel → https://goo.gle/GCP
Product: BigQuery, Kubeflow, Kubeflow Pipelines; fullname: Stephanie Wong;
#Kubeflow101
Duration: 00:04:59
Publisher: Google Cloud
You can watch this video also at the source.
